Method for monitoring component life

Abstract

Methods and systems are provided for reliably prognosing a vehicle component, such as a vehicle battery or an intake air filter. A state of degradation of the component is predicted based on a metric that is derived from a sensed vehicle operating parameter, the parameter selected based on the component being diagnosed, as well as based on past driving history and future driving predictions. The predicted state of degradation is then converted into an estimate of time or distance remaining before the component needs to serviced, and displayed to the vehicle operator.

Description

FIELD[0001]The present application relates to methods performed in vehicles, such as hybrid vehicles, for estimating the remaining life of a vehicle component using statistical predictions.BACKGROUND AND SUMMARY[0002]Vehicles include various components which degrade at different rates and have to be serviced at different times. In addition, the degradation rate of each component may be affected by multiple parameters, some of which are overlapping with other components while others are non-overlapping. For example, in hybrid electric vehicles, a system battery may degrade based on the rate of battery usage, the age of the battery, temperature conditions, the nature of the battery, etc. As another example, an air filter coupled to the engine intake may degrade based on the age of the filter, air quality, ambient weather conditions, etc.[0003]Various approaches have been developed to predict the state of health of a vehicle component. One example approach is shown by Uchida in U.S. Pa...

AI Technical Summary

Benefits of technology

This patent describes a method for predicting when a vehicle component will fail and alerting the operator of the vehicle in advance. It does not rely on complex calculations and can predict the remaining life of a component using data sensed on the vehicle and driving statistics. This allows the operator to plan for maintenance and avoid component failure. The method uses a recursive estimation of statistical features to predict the remaining life of a component with less computation intensity and ensures healthy operation for the estimated remaining life. The service reminders are provided early and conveniently through an online estimate, which can help prevent breakdowns and improve vehicle performance.

Problems solved by technology

As another example, an air filter coupled to the engine intake may degrade based on the age of the filter, air quality, ambient weather conditions, etc.

However the inventors herein have identified various issues with such approaches.

As one example, the above approaches rely on statistical analyses that can be computationally intensive.

Consequently, they may require extensive memory and processor resources to assess the health of the battery.

Reliance on sensors, which themselves are subject to wear and tear, can cause inaccuracy in the state of health estimation.

In addition, the approach does not accurately account for the effect of temperature on the internal resistance and capacitance of the battery, as the battery ages.

As yet another example, an operator may not be able to comprehend how much battery degradation has occurred when the battery health is indicated in terms of a number of remaining useful cycles.

This may be particularly difficult when the battery is part of a hybrid vehicle where the engine automatically meets the driver demand when the battery is not able to.

As a result, the vehicle operator may not be able to replace or service the battery before it is fully degraded, compromising vehicle operation.

Further, the vehicle operator may not be able to timely modify their driving characteristics to avert battery degradation.

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